Generation of synthetic images for training a neural network model

ABSTRACT

Training deep neural networks requires a large amount of labeled training data. Conventionally, labeled training data is generated by gathering real images that are manually labelled which is very time-consuming. Instead of manually labelling a training dataset, domain randomization technique is used generate training data that is automatically labeled. The generated training data may be used to train neural networks for object detection and segmentation (labelling) tasks. In an embodiment, the generated training data includes synthetic input images generated by rendering three-dimensional (3D) objects of interest in a 3D scene. In an embodiment, the generated training data includes synthetic input images generated by rendering 3D objects of interest on a 2D background image. The 3D objects of interest are objects that a neural network is trained to detect and/or label.

CLAIM OF PRIORITY

This application claims the benefit of U.S. application Ser. No.16/256,820 (Attorney Docket No. 510979) titled “GENERATION OF SYNTHETICIMAGES FOR TRAINING A NEURAL NETWORK MODEL,” filed Jan. 24, 2019 thatclaims the benefit of U.S. Provisional Application No. 62/630,722(Attorney Docket No. NVIDP1212+/17BL0293US01) titled “A SYSTEM ANDMETHOD FOR TRAINING A COMPUTER VISION SYSTEM USING NON-REALISTICSYNTHETIC DATA,” filed Feb. 14, 2018, the entire contents of bothapplications are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to synthetic images, and moreparticularly to generating synthetic images for training a neuralnetwork model.

BACKGROUND

Training deep neural networks requires a large amount of labeledtraining data. Conventionally, labeled training data is generated bygathering real images that are manually labelled which is verytime-consuming. There is a need for addressing these issues and/or otherissues associated with the prior art.

SUMMARY

A domain randomization technique for generate training data that isautomatically labeled is described. The generated training data may beused to train neural networks for object detection and segmentationtasks. In an embodiment, the generated training data includes syntheticinput images generated by rendering three-dimensional (3D) objects ofinterest in a 3D scene. In an embodiment, the generated training dataincludes synthetic input images generated by rendering 3D objects ofinterest on a 2D background image. The 3D objects of interest areobjects that a neural network is trained to detect and/or segment.

A method, computer readable medium, and system are disclosed forgenerating synthetic images for training a neural network model. Athree-dimensional (3D) object of interest is rendered to produce arendered image of the object of interest, where an input image comprisesthe rendered image of the object of interest and a background image.Task-specific training data corresponding to the object of interest iscomputed and the task-specific training data corresponding to the objectof interest and the input image are included as a test pair in atraining dataset for training a neural network.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a block diagram of a labeled training datageneration system, in accordance with an embodiment.

FIG. 1B illustrates a background image, rendered images of an object ofinterest, and an input image with task-specific training data, inaccordance with an embodiment.

FIG. 1C illustrates a flowchart of a method for generating labeledtraining data, in accordance with an embodiment.

FIG. 2A illustrates a block diagram of another labeled training datageneration system, in accordance with an embodiment.

FIG. 2B illustrates the background image, rendered 3D geometric shapes,and another input image with the task-specific training data, inaccordance with an embodiment.

FIG. 2C illustrates a flowchart of another method for generating labeledtraining data, in accordance with an embodiment.

FIG. 2D illustrates a block diagram of neural network model trainingsystem, in accordance with an embodiment.

FIG. 3 illustrates a parallel processing unit, in accordance with anembodiment.

FIG. 4A illustrates a general processing cluster within the parallelprocessing unit of FIG. 3, in accordance with an embodiment.

FIG. 4B illustrates a memory partition unit of the parallel processingunit of FIG. 3, in accordance with an embodiment.

FIG. 5A illustrates the streaming multi-processor of FIG. 4A, inaccordance with an embodiment.

FIG. 5B is a conceptual diagram of a processing system implemented usingthe PPU of FIG. 3, in accordance with an embodiment.

FIG. 5C illustrates an exemplary system in which the variousarchitecture and/or functionality of the various previous embodimentsmay be implemented.

FIG. 6 is a conceptual diagram of a graphics processing pipelineimplemented by the PPU of FIG. 3, in accordance with an embodiment.

DETAILED DESCRIPTION

Training deep neural networks requires a large amount of labeledtraining data. A domain randomization technique to generate trainingdata that is automatically labeled is described. The generated trainingdata may be used to train neural networks for object detection andsegmentation tasks. Domain randomization intentionally abandonsphotorealism by randomly perturbing the environment innon-photorealistic ways (e.g., by adding random textures) to force aneural network model to learn to focus on the essential features ofimages. More specifically, the neural network model is trained to detectobjects of interest and ignore other objects in the images. In anembodiment, the generated training data is used to train a neuralnetwork model for the task of object detection. In an embodiment, thegenerated training data is used to train a neural network model for thetask of instance segmentation. In an embodiment, the generated trainingdata is used to train a neural network model for the task of semanticsegmentation.

FIG. 1A illustrates a block diagram of a labeled training datageneration system 100, in accordance with an embodiment. The labeledtraining data generation system 100 includes a graphics processing unit(GPU) 110, a task-specific training data computation unit 115, and aninput image generator 120. Although the labeled training data generationsystem 100 is described in the context of processing units, one or moreof the GPU 110, the task-specific training data computation unit 115,and the input image generator 120 may be performed by a program, customcircuitry, or by a combination of custom circuitry and a program. Forexample, the task-specific training data computation unit 115 may beimplemented by the GPU 110 or an additional GPU 110, CPU (centralprocessing unit), or any processor capable of computing task-specifictraining data. In an embodiment, parallel processing unit (PPU) 300 ofFIG. 3 is configured to implement the labeled training data generationsystem 100. Furthermore, persons of ordinary skill in the art willunderstand that any system that performs the operations of the labeledtraining data generation system 100 is within the scope and spirit ofembodiments of the present disclosure.

The GPU 110 receives a 3D synthetic object (object of interest) andrendering parameters. The GPU 110 processes the 3D object according tothe rendering parameters to generate a rendered image of the 3D object,specifically, a rendered image of the object of interest. Importantly,the rendered image is an image of a synthetic object of interest and isnot a photorealistic image or an object extracted from a photorealisticimage. The rendering parameters may specify a position and/ororientation of the object of interest in a 3D scene, a position and/ororientation of a virtual camera, one or more texture maps, one or morelights including color, type, intensity, position and/or orientation,and the like. In an embodiment, the object of interest may be renderedaccording to different rendering parameters to produce additionalrendered images of the object of interest. In an embodiment, one or moredifferent objects of interest may be rendered according to the same ordifferent rendering parameters to produce additional rendered images ofobjects of interest.

The task-specific training data computation unit 115 receives therendered image(s) of the object(s) of interest and computestask-specific training data. In an embodiment, the task is objectdetection and the training data computation unit 115 computes boundingboxes for the rendered image(s) of the object(s) of interest. Thetraining data computation unit 115 may receive location coordinates fromthe input image generator 120 defining a location in the input image foreach rendered image of an object of interest. In an embodiment, thetask-specific training data comprise a location and dimensions of abounding box enclosing each rendered image of an object of interest.

In an embodiment, the task is segmentation and the training datacomputation unit 115 determines an object identifier for each renderedimage of an object of interest and computes the task-specific trainingdata as a segmentation map corresponding to the input image. Forsemantic segmentation, a different object identifier may be determinedfor each object of interest and the segmentation map comprises the inputimage, where each pixel that is covered by a rendered image is coloredaccording to the object identifier determined for the object ofinterest. For instance segmentation, a different object identifier maybe determined for each instance of a rendered image in the input imageand the segmentation map comprises the input image, where each pixelthat is covered by a rendered image is colored according to the objectidentifier determined for the instance.

The input image generator 120 receives the rendered image of the objectof interest and a background image. The input image generator 120constructs an input image that combines the background image and therendered image(s) of the object(s) of interest. The input image ispaired with the task-specific training data to produce a test pair forgenerated labeled training data for training a neural network model. Therendered objects of interest are 3D synthetic objects that a neuralnetwork model may be trained to detect and/or segment.

In an embodiment, the input image generator 120 combines or compositesthe rendered image(s) of the object(s) of interest with atwo-dimensional (2D) background image to produce the input image. In anembodiment, the background image is a synthetic image. In an embodiment,the background image is a photorealistic image. In an embodiment, a 3Dscene is rendered by the GPU 110 or another processor to produce thebackground image. In an embodiment, the input image generator 120 isomitted and the GPU 110 renders the object(s) of interest within a 3Dscene corresponding to the background image, generating the input imagecomprising both the background image and the rendered image(s) of theobject(s) of interest.

More illustrative information will now be set forth regarding variousoptional architectures and features with which the foregoing frameworkmay be implemented, per the desires of the user. It should be stronglynoted that the following information is set forth for illustrativepurposes and should not be construed as limiting in any manner. Any ofthe following features may be optionally incorporated with or withoutthe exclusion of other features described.

Training and testing a deep neural network is a time-consuming andexpensive task which typically involves collecting and manuallyannotating a large amount of data for supervised learning. Thisrequirement is problematic when the task demands either expertknowledge, labels that are difficult to specify manually, or images thatare difficult to capture in large quantities with sufficient variety.For example, 3D poses or pixelwise segmentation can take a substantialamount of time for a human to manually label a single image.

FIG. 1B illustrates a background image, rendered images of a 3D objectof interest, and an input image with task-specific training data, inaccordance with an embodiment. In an embodiment, the background image isa 2D image. The background image may be produced by rendering a 3Dscene. In an embodiment, the background image is selected from a set ofbackground images. In an embodiment, the background image is randomlyselected by the labeled training data generation system 100.

As shown in FIG. 1B, a 3D synthetic object of interest (automobile) isrendered to produce three different rendered images of the object ofinterest. Note that at least a portion of the rendering parameters varyfor each one of the rendered images. For example, a texture map isapplied to a first rendering of the 3D synthetic object, a first coloris used to render the body of the 3D synthetic object for a secondrendering, and a second color is used to render the body of the 3Dsynthetic object for a third rendering. In addition to or in lieu ofchanging the color or applying a texture to the 3D synthetic object, theorientation of the 3D synthetic object may vary. In an embodiment, the3D synthetic object is selected from a set of 3D synthetic objects. Inan embodiment, one or more of the 3D synthetic objects are selected bythe labeled training data generation system 100.

The rendering parameters specify aspects of a 3D scene including the 3Dsynthetic object(s) of interest to be rendered and, therefore, mayaffect the appearance of the rendered 3D synthetic object(s) ofinterest. In an embodiment, the position of the virtual camera withrespect to the 3D scene (e.g., azimuth, elevation, etc.) is defined bythe rendering parameters. In an embodiment, an orientation of thevirtual camera with respect to the 3D scene (e.g., pan, tilt, and roll)is defined by the rendering parameters. In an embodiment, the positionand/or the orientation of the virtual camera with respect to the 3Dscene (e.g., pan, tilt, and roll) is randomly determined by the labeledtraining data generation system 100. In an embodiment, a number andposition of one or more point lights is defined by the renderingparameters. In an embodiment, the number and position of one or morepoint lights is randomly determined by the labeled training datageneration system 100. In an embodiment, a planar light for ambientlight is defined by the rendering parameters. In an embodiment,visibility of a ground plane in the 3D scene is defined by the renderingparameters.

The input image generator 120 constructs the input image by combiningthe rendered object(s) of interest and the background image, positioningthe rendered object(s) of interest at various positions within the inputimage. Each rendered object of interest may be scaled in size and/orrotated. In an embodiment, the number of rendered objects of interestand the position, scale, and/or rotation for each rendered object ofinterest is defined by the rendering parameters. In an embodiment, thenumber of rendered objects of interest and the position, scale, and/orrotation for each rendered object of interest is randomly determined bythe input image generator 120. As shown in FIG. 1B, the task-specifictraining data computed by the task-specific training data computationunit 115 for each rendered object of interest is a bounding box. Thetask-specific training data is not included as part of the input imagedata, but is instead paired with the input image data. During supervisedtraining of a neural network model, the task-specific training data isground truth labels corresponding to the rendered object(s) of interestthat are compared with an output generated by the neural network modelwhen the input image is processed by the neural network model.

FIG. 1C illustrates a flowchart of a method 130 for generating labeledtraining data, in accordance with an embodiment. Although method 130 isdescribed in the context of a processing unit, the method 130 may alsobe performed by a program, custom circuitry, or by a combination ofcustom circuitry and a program. For example, the method 130 may beexecuted by a GPU (graphics processing unit), CPU (central processingunit), or any processor capable of rendering 3D objects, constructingimages, and computing labels. Furthermore, persons of ordinary skill inthe art will understand that any system that performs method 130 iswithin the scope and spirit of embodiments of the present disclosure.

At step 135, the GPU 110 renders a 3D object of interest to produce arendered image of the object of interest, where an input image comprisesthe rendered image of the object of interest and a background image. The3D object of interest is rendered according to the rendering parameters.In an embodiment, the GPU 110 renders the 3D object of interest usingdifferent rendering parameters to produce additional rendered images ofthe object of interest. In an embodiment, the GPU 110 renders adifferent 3D object of interest using the same rendering parameters asare used to render the 3D object of interest or using differentrendering parameters to produce additional rendered images of objects ofinterest.

At step 140, task-specific training data corresponding to the object ofinterest is computed by the task-specific training data computation unit115. In an embodiment, the task-specific training data is annotationsindicating locations of the rendered object of interest. For example,the locations may be (x, y, width, height) coordinates of 2D boundingboxes enclosing each rendered object of interest. In an embodiment, forsegmentation, the task-specific training data is the rendered objects ofinterest having each pixel within a rendered object of interest replacedwith an object identifier associated with the rendered object ofinterest. The object identifier may be shared with other objects in thesame class or may be unique for each rendered object of interest.

At step 145, the task-specific training data corresponding to the objectof interest and the input image are included as a test pair in atraining dataset for training a neural network. In an embodiment, thetraining dataset is stored in a memory. In an embodiment, the trainingdataset that is generated by the labeled training data generation system100 is simultaneously used to train a neural network model. In otherwords, the training of the neural network model is performedconcurrently with generation of the labeled training data.

To better enable a neural network model to learn to ignore objects in aninput image that are not of interest, a random number of rendered 3Dgeometric shapes may be inserted into the input image. The renderedgeometric shapes may be referred to as flying distractors. In anembodiment, the geometric shapes are rendered according to the renderingparameters. In an embodiment, the generated training data includessynthetic input images including the rendered 3D objects of interest andrendered 3D flying distractors. In an embodiment a number, types,colors, and scales of the geometric shapes, selected from a set of 3Dmodels (cones, pyramids, spheres, cylinders, partial toroids, arrows,pedestrians, trees, etc.).

FIG. 2A illustrates a block diagram of another labeled training datageneration system 200, in accordance with an embodiment. The labeledtraining data generation system 200 includes a graphics processing unit(GPU) 110, the task-specific training data computation unit 115, and aninput image generator 220. Although the labeled training data generationsystem 200 is described in the context of processing units, one or moreof the GPU 110, the task-specific training data computation unit 115,and the input image generator 220 may be performed by a program, customcircuitry, or by a combination of custom circuitry and a program. Forexample, the task-specific training data computation unit 115 may beimplemented by the GPU 110 or an additional GPU 110, CPU (centralprocessing unit), or any processor capable of computing task-specifictraining data. In an embodiment, parallel processing unit (PPU) 300 ofFIG. 3 is configured to implement the labeled training data generationsystem 200. Furthermore, persons of ordinary skill in the art willunderstand that any system that performs the operations of the labeledtraining data generation system 200 is within the scope and spirit ofembodiments of the present disclosure.

The GPU 110 renders the 3D objects of interest as previously describedto produce rendered images of objects of interest. The GPU 110 alsorenders the geometric shapes according to the rendering parameters toproduce rendered images of the geometric shapes. Importantly, a renderedimage of the geometric shape is an image of a synthetic geometric shapeand is not a photorealistic image or an object extracted from aphotorealistic image. The rendering parameters may specify a positionand/or orientation of the geometric shape in a 3D scene, a positionand/or orientation of a virtual camera, one or more texture maps, one ormore lights including color, type, intensity, position and/ororientation, and the like. In an embodiment, the geometric shape may berendered according to different rendering parameters to produceadditional rendered images of the object of interest. In an embodiment,one or more different geometric shape may be rendered according to thesame or different rendering parameters to produce additional renderedimages of the geometric shape.

The task-specific training data computation unit 115 does not receivethe rendered image(s) of the geometric shapes because task-specifictraining data is computed based only on the rendered object(s) ofinterest. In an embodiment, the task is object detection and the neuralnetwork model that is trained should ignore the rendered image(s) of thegeometric shape(s) and detect the rendered image(s) of the object(s) ofinterest. In an embodiment, the task is segmentation and the neuralnetwork model that is trained should ignore the rendered image(s) of thegeometric shape(s) and segment only the rendered image(s) of theobject(s) of interest.

The input image generator 220 receives the rendered image(s) of thegeometric shape(s), the rendered image(s) of the object(s) of interest,and a background image. The input image generator 220 constructs aninput image that combines the background image, the rendered image(s) ofthe object(s) of interest, and the rendered image(s) of the geometricshape(s). The input image is paired with the task-specific training datato produce a test pair for generated labeled training data for traininga neural network model. The rendered objects of interest are 3Dsynthetic objects that a neural network model may be trained to detectand/or segment. The rendered geometric shapes are 3D synthetic geometricshapes that a neural network model may be trained to ignore.

FIG. 2B illustrates the background image, rendered 3D geometric shapes,and an input image with the task-specific training data, in accordancewith an embodiment. In an embodiment, the background image is a 2Dimage. As shown in FIG. 2B, 3D geometric shapes are rendered to producethree different rendered images of the object of interest. Note that atleast a portion of the rendering parameters vary for each one of therendered images. For example, different texture maps are applied to eachone of the 3D geometric shapes. A color of and/or the orientation ofeach 3D geometric shape may vary. In an embodiment, each 3D geometricshape is selected from a set of 3D synthetic geometric shapes. In anembodiment, one or more of the 3D geometric shape are selected by thelabeled training data generation system 100. The rendering parametersspecify aspects of a 3D scene including the 3D geometric shape(s) to berendered and, therefore, may affect the appearance of the rendered 3Dgeometric shape(s).

The input image generator 220 constructs the input image by combiningthe rendered object(s) of interest, the rendered geometric shapes, andthe background image, positioning the rendered object(s) of interest andthe rendered geometric shapes at various positions within the inputimage. Each the rendered geometric shape may be scaled in size and/orrotated. In an embodiment, the number of the rendered geometric shapesand the position, scale, and/or rotation for each rendered object ofinterest is defined by the rendering parameters. In an embodiment, thenumber of the rendered geometric shapes and the position, scale, and/orrotation for each rendered geometric shape is randomly determined by theinput image generator 220. As shown in FIG. 2B, the task-specifictraining data computed by the task-specific training data computationunit 115 for each rendered object of interest is a bounding box. Note,no task-specific training data is computed for the rendered geometricshapes.

A particular rendered object of interest may be occluded by one or moreother rendered objects of interest and/or rendered geometric shapes.When the portion of a rendered object of interest that is occluded isgreater than a predetermined threshold value, the task-specific trainingdata corresponding to the rendered object of interest may be modified.In one embodiment, when 98% of a rendered object of interest isoccluded, the task-specific training data corresponding to the renderedobject of interest is omitted from the task-specific training data.

The labeled training data generation system 200 generates test pairs fortraining deep neural networks for object detection using synthetic 3Dobjects. To synthesize the variability in real-world data, the labeledtraining data generation system 200 relies upon the technique of domainrandomization, in which the rendering parameters—such as lighting, pose,object textures, etc.—are randomized or specified to producenon-realistic input images to force a neural network model to learn theessential features of the object of interest.

FIG. 2C illustrates a flowchart of another method 230 for generatinglabeled training data, in accordance with an embodiment. Although method230 is described in the context of a processing unit, the method 230 mayalso be performed by a program, custom circuitry, or by a combination ofcustom circuitry and a program. For example, the method 230 may beexecuted by a GPU (graphics processing unit), CPU (central processingunit), or any processor capable of rendering 3D objects, constructingimages, and computing labels. Furthermore, persons of ordinary skill inthe art will understand that any system that performs method 230 iswithin the scope and spirit of embodiments of the present disclosure.

At step 135, the GPU 110 renders one or more 3D geometric shapes toproduce one or more rendered geometric shapes. The 3D geometric shapesare rendered according to the rendering parameters. In an embodiment,the GPU 110 renders at least one of the 3D geometric shapes usingdifferent rendering parameters to produce additional rendered images ofthe geometric shape. In an embodiment, the GPU 110 renders different 3Dgeometric shapes using the same rendering parameters or using differentrendering parameters to produce different rendered images of geometricshapes.

At step 240, the GPU 110 renders a 3D object of interest according toone or more rendering parameters to produce a rendered image of theobject of interest. At step 245, the input image generator 220constructs an input image comprising the rendered image of the object ofinterest, the one or more rendered geometric shapes, and a backgroundimage.

Steps 140 and 145 are completed as previously described in conjunctionwith FIG. 1B. In an embodiment, the training dataset that is generatedby the labeled training data generation system 200 is simultaneouslyused to train a neural network model. In other words, the training ofthe neural network model is performed concurrently with generation ofthe training dataset.

Incorporation of the rendered geometric shapes into the input imageimproves object detection and/or estimation accuracy for neural networkmodels trained using the training dataset generated by the labeledtraining data generation system 200. The various rendering parametersmay have varying influences on the performance of a neural network modeltrained using the training dataset. In an embodiment, a neural networkmodel is trained using only the training dataset that is generated usingsynthetic objects of interest. In an embodiment, a neural network modelis trained using the task-specific training dataset that is generatedusing synthetic objects of interest and a lesser amount of real datathat is labeled. In an embodiment, during training, one or more of thefollowing data augmentations are applied to the training dataset: randombrightness, random contrast, random Gaussian noise, random flips, randomresizing, box jitter, and random crop.

FIG. 2D illustrates a block diagram of neural network model trainingsystem 250, in accordance with an embodiment. The neural network modeltraining system 250 includes a neural network model 260 and a lossfunction unit 270. Although the neural network model training system 250is described in the context of processing units, one or more of theneural network model 260 and the loss function unit 270 may be performedby a program, custom circuitry, or by a combination of custom circuitryand a program. For example, the neural network model 260 may beimplemented by a GPU 110, CPU (central processing unit), or anyprocessor capable of implementing a neural network model. In anembodiment, parallel processing unit (PPU) 300 of FIG. 3 is configuredto implement the neural network model training system 250. Furthermore,persons of ordinary skill in the art will understand that any systemthat performs the operations of the neural network model training system250 is within the scope and spirit of embodiments of the presentdisclosure.

During training, the input images for test pairs included in thetraining dataset are processed, according to weights, by the neuralnetwork model 260 to generate output data. The output data andtask-specific training data for the test pairs are processed by the lossfunction unit 270. The loss function unit 270 generates updated weightsto reduce differences between the task-specific training data and theoutput data. When the differences are reduced to a predetermined value,training is complete. In an embodiment, the neural network model 260 isa convolutional neural network (CNN), recurrent CNN, feed-forward CNN, aregion-based fully convolutional neural network, or the like.

Although the input images may appear crude (and almost cartoonish) andnot aesthetically pleasing, this apparent limitation is arguably anasset: Not only are the input images orders of magnitude faster tocreate (with less expertise required) compared with manual labeling ofphotorealistic images, but the input images include variations thatforce a deep neural network to focus on the important structure of theproblem at hand rather than details that may or may not be present inreal images at test time. Furthermore, the domain randomization-basedapproach generation of training dataset with a large amount of varietyis easily achieved, in contrast with existing conventional trainingdataset generation techniques. Inexpensive synthetic data may be used togenerate labeled training datasets for training neural networks whileavoiding the need to collect large amounts of hand-annotated real-worlddata or to generate high-fidelity synthetic worlds—both of which remainbottlenecks for many applications.

The training dataset generated by the labeled training data generationsystem 100 or 200 may be used to train a neural network model toaccomplish complex tasks such as object detection with performancecomparable to more labor-intensive (and therefore more expensive)datasets. By randomly perturbing the synthetic images during training,domain randomization intentionally abandons photorealism to force theneural network to learn to focus on the relevant features. Withfine-tuning on real images, domain randomization may outperform morephotorealistic datasets and may improve upon training results obtainedusing real data alone.

Parallel Processing Architecture

FIG. 3 illustrates a parallel processing unit (PPU) 300, in accordancewith an embodiment. In an embodiment, the PPU 300 is a multi-threadedprocessor that is implemented on one or more integrated circuit devices.The PPU 300 is a latency hiding architecture designed to process manythreads in parallel. A thread (e.g., a thread of execution) is aninstantiation of a set of instructions configured to be executed by thePPU 300. In an embodiment, the PPU 300 is a graphics processing unit(GPU) configured to implement a graphics rendering pipeline forprocessing three-dimensional (3D) graphics data in order to generatetwo-dimensional (2D) image data for display on a display device such asa liquid crystal display (LCD) device. In other embodiments, the PPU 300may be utilized for performing general-purpose computations.

While one exemplary parallel processor is provided herein forillustrative purposes, it should be strongly noted that such processoris set forth for illustrative purposes only, and that any processor maybe employed to supplement and/or substitute for the same.

One or more PPUs 300 may be configured to accelerate thousands of HighPerformance Computing (HPC), data center, and machine learningapplications. The PPU 300 may be configured to accelerate numerous deeplearning systems and applications including autonomous vehicleplatforms, deep learning, high-accuracy speech, image, and textrecognition systems, intelligent video analytics, molecular simulations,drug discovery, disease diagnosis, weather forecasting, big dataanalytics, astronomy, molecular dynamics simulation, financial modeling,robotics, factory automation, real-time language translation, onlinesearch optimizations, and personalized user recommendations, and thelike.

As shown in FIG. 3, the PPU 300 includes an Input/Output (I/O) unit 305,a front end unit 315, a scheduler unit 320, a work distribution unit325, a hub 330, a crossbar (Xbar) 370, one or more general processingclusters (GPCs) 350, and one or more memory partition units 380. The PPU300 may be connected to a host processor or other PPUs 300 via one ormore high-speed NVLink 310 interconnect. The PPU 300 may be connected toa host processor or other peripheral devices via an interconnect 302.The PPU 300 may also be connected to a local memory comprising a numberof memory devices 304. In an embodiment, the local memory may comprise anumber of dynamic random access memory (DRAM) devices. The DRAM devicesmay be configured as a high-bandwidth memory (HBM) subsystem, withmultiple DRAM dies stacked within each device.

The NVLink 310 interconnect enables systems to scale and include one ormore PPUs 300 combined with one or more CPUs, supports cache coherencebetween the PPUs 300 and CPUs, and CPU mastering. Data and/or commandsmay be transmitted by the NVLink 310 through the hub 330 to/from otherunits of the PPU 300 such as one or more copy engines, a video encoder,a video decoder, a power management unit, etc. (not explicitly shown).The NVLink 310 is described in more detail in conjunction with FIG. 5B.

The I/O unit 305 is configured to transmit and receive communications(e.g., commands, data, etc.) from a host processor (not shown) over theinterconnect 302. The I/O unit 305 may communicate with the hostprocessor directly via the interconnect 302 or through one or moreintermediate devices such as a memory bridge. In an embodiment, the I/Ounit 305 may communicate with one or more other processors, such as oneor more the PPUs 300 via the interconnect 302. In an embodiment, the I/Ounit 305 implements a Peripheral Component Interconnect Express (PCIe)interface for communications over a PCIe bus and the interconnect 302 isa PCIe bus. In alternative embodiments, the I/O unit 305 may implementother types of well-known interfaces for communicating with externaldevices.

The I/O unit 305 decodes packets received via the interconnect 302. Inan embodiment, the packets represent commands configured to cause thePPU 300 to perform various operations. The I/O unit 305 transmits thedecoded commands to various other units of the PPU 300 as the commandsmay specify. For example, some commands may be transmitted to the frontend unit 315. Other commands may be transmitted to the hub 330 or otherunits of the PPU 300 such as one or more copy engines, a video encoder,a video decoder, a power management unit, etc. (not explicitly shown).In other words, the I/O unit 305 is configured to route communicationsbetween and among the various logical units of the PPU 300.

In an embodiment, a program executed by the host processor encodes acommand stream in a buffer that provides workloads to the PPU 300 forprocessing. A workload may comprise several instructions and data to beprocessed by those instructions. The buffer is a region in a memory thatis accessible (e.g., read/write) by both the host processor and the PPU300. For example, the I/O unit 305 may be configured to access thebuffer in a system memory connected to the interconnect 302 via memoryrequests transmitted over the interconnect 302. In an embodiment, thehost processor writes the command stream to the buffer and thentransmits a pointer to the start of the command stream to the PPU 300.The front end unit 315 receives pointers to one or more command streams.The front end unit 315 manages the one or more streams, reading commandsfrom the streams and forwarding commands to the various units of the PPU300.

The front end unit 315 is coupled to a scheduler unit 320 thatconfigures the various GPCs 350 to process tasks defined by the one ormore streams. The scheduler unit 320 is configured to track stateinformation related to the various tasks managed by the scheduler unit320. The state may indicate which GPC 350 a task is assigned to, whetherthe task is active or inactive, a priority level associated with thetask, and so forth. The scheduler unit 320 manages the execution of aplurality of tasks on the one or more GPCs 350.

The scheduler unit 320 is coupled to a work distribution unit 325 thatis configured to dispatch tasks for execution on the GPCs 350. The workdistribution unit 325 may track a number of scheduled tasks receivedfrom the scheduler unit 320. In an embodiment, the work distributionunit 325 manages a pending task pool and an active task pool for each ofthe GPCs 350. The pending task pool may comprise a number of slots(e.g., 32 slots) that contain tasks assigned to be processed by aparticular GPC 350. The active task pool may comprise a number of slots(e.g., 4 slots) for tasks that are actively being processed by the GPCs350. As a GPC 350 finishes the execution of a task, that task is evictedfrom the active task pool for the GPC 350 and one of the other tasksfrom the pending task pool is selected and scheduled for execution onthe GPC 350. If an active task has been idle on the GPC 350, such aswhile waiting for a data dependency to be resolved, then the active taskmay be evicted from the GPC 350 and returned to the pending task poolwhile another task in the pending task pool is selected and scheduledfor execution on the GPC 350.

The work distribution unit 325 communicates with the one or more GPCs350 via XBar 370. The XBar 370 is an interconnect network that couplesmany of the units of the PPU 300 to other units of the PPU 300. Forexample, the XBar 370 may be configured to couple the work distributionunit 325 to a particular GPC 350. Although not shown explicitly, one ormore other units of the PPU 300 may also be connected to the XBar 370via the hub 330.

The tasks are managed by the scheduler unit 320 and dispatched to a GPC350 by the work distribution unit 325. The GPC 350 is configured toprocess the task and generate results. The results may be consumed byother tasks within the GPC 350, routed to a different GPC 350 via theXBar 370, or stored in the memory 304. The results can be written to thememory 304 via the memory partition units 380, which implement a memoryinterface for reading and writing data to/from the memory 304. Theresults can be transmitted to another PPU 304 or CPU via the NVLink 310.In an embodiment, the PPU 300 includes a number U of memory partitionunits 380 that is equal to the number of separate and distinct memorydevices 304 coupled to the PPU 300. A memory partition unit 380 will bedescribed in more detail below in conjunction with FIG. 4B.

In an embodiment, a host processor executes a driver kernel thatimplements an application programming interface (API) that enables oneor more applications executing on the host processor to scheduleoperations for execution on the PPU 300. In an embodiment, multiplecompute applications are simultaneously executed by the PPU 300 and thePPU 300 provides isolation, quality of service (QoS), and independentaddress spaces for the multiple compute applications. An application maygenerate instructions (e.g., API calls) that cause the driver kernel togenerate one or more tasks for execution by the PPU 300. The driverkernel outputs tasks to one or more streams being processed by the PPU300. Each task may comprise one or more groups of related threads,referred to herein as a warp. In an embodiment, a warp comprises 32related threads that may be executed in parallel. Cooperating threadsmay refer to a plurality of threads including instructions to performthe task and that may exchange data through shared memory. Threads andcooperating threads are described in more detail in conjunction withFIG. 5A.

FIG. 4A illustrates a GPC 350 of the PPU 300 of FIG. 3, in accordancewith an embodiment. As shown in FIG. 4A, each GPC 350 includes a numberof hardware units for processing tasks. In an embodiment, each GPC 350includes a pipeline manager 410, a pre-raster operations unit (PROP)415, a raster engine 425, a work distribution crossbar (WDX) 480, amemory management unit (MMU) 490, and one or more Data ProcessingClusters (DPCs) 420. It will be appreciated that the GPC 350 of FIG. 4Amay include other hardware units in lieu of or in addition to the unitsshown in FIG. 4A.

In an embodiment, the operation of the GPC 350 is controlled by thepipeline manager 410. The pipeline manager 410 manages the configurationof the one or more DPCs 420 for processing tasks allocated to the GPC350. In an embodiment, the pipeline manager 410 may configure at leastone of the one or more DPCs 420 to implement at least a portion of agraphics rendering pipeline. For example, a DPC 420 may be configured toexecute a vertex shader program on the programmable streamingmultiprocessor (SM) 440. The pipeline manager 410 may also be configuredto route packets received from the work distribution unit 325 to theappropriate logical units within the GPC 350. For example, some packetsmay be routed to fixed function hardware units in the PROP 415 and/orraster engine 425 while other packets may be routed to the DPCs 420 forprocessing by the primitive engine 435 or the SM 440. In an embodiment,the pipeline manager 410 may configure at least one of the one or moreDPCs 420 to implement a neural network model and/or a computingpipeline.

The PROP unit 415 is configured to route data generated by the rasterengine 425 and the DPCs 420 to a Raster Operations (ROP) unit, describedin more detail in conjunction with FIG. 4B. The PROP unit 415 may alsobe configured to perform optimizations for color blending, organizepixel data, perform address translations, and the like.

The raster engine 425 includes a number of fixed function hardware unitsconfigured to perform various raster operations. In an embodiment, theraster engine 425 includes a setup engine, a coarse raster engine, aculling engine, a clipping engine, a fine raster engine, and a tilecoalescing engine. The setup engine receives transformed vertices andgenerates plane equations associated with the geometric primitivedefined by the vertices. The plane equations are transmitted to thecoarse raster engine to generate coverage information (e.g., an x,ycoverage mask for a tile) for the primitive. The output of the coarseraster engine is transmitted to the culling engine where fragmentsassociated with the primitive that fail a z-test are culled, andtransmitted to a clipping engine where fragments lying outside a viewingfrustum are clipped. Those fragments that survive clipping and cullingmay be passed to the fine raster engine to generate attributes for thepixel fragments based on the plane equations generated by the setupengine. The output of the raster engine 425 comprises fragments to beprocessed, for example, by a fragment shader implemented within a DPC420.

Each DPC 420 included in the GPC 350 includes an M-Pipe Controller (MPC)430, a primitive engine 435, and one or more SMs 440. The MPC 430controls the operation of the DPC 420, routing packets received from thepipeline manager 410 to the appropriate units in the DPC 420. Forexample, packets associated with a vertex may be routed to the primitiveengine 435, which is configured to fetch vertex attributes associatedwith the vertex from the memory 304. In contrast, packets associatedwith a shader program may be transmitted to the SM 440.

The SM 440 comprises a programmable streaming processor that isconfigured to process tasks represented by a number of threads. Each SM440 is multi-threaded and configured to execute a plurality of threads(e.g., 32 threads) from a particular group of threads concurrently. Inan embodiment, the SM 440 implements a SIMD (Single-Instruction,Multiple-Data) architecture where each thread in a group of threads(e.g., a warp) is configured to process a different set of data based onthe same set of instructions. All threads in the group of threadsexecute the same instructions. In another embodiment, the SM 440implements a SIMT (Single-Instruction, Multiple Thread) architecturewhere each thread in a group of threads is configured to process adifferent set of data based on the same set of instructions, but whereindividual threads in the group of threads are allowed to diverge duringexecution. In an embodiment, a program counter, call stack, andexecution state is maintained for each warp, enabling concurrencybetween warps and serial execution within warps when threads within thewarp diverge. In another embodiment, a program counter, call stack, andexecution state is maintained for each individual thread, enabling equalconcurrency between all threads, within and between warps. Whenexecution state is maintained for each individual thread, threadsexecuting the same instructions may be converged and executed inparallel for maximum efficiency. The SM 440 will be described in moredetail below in conjunction with FIG. 5A.

The MMU 490 provides an interface between the GPC 350 and the memorypartition unit 380. The MMU 490 may provide translation of virtualaddresses into physical addresses, memory protection, and arbitration ofmemory requests. In an embodiment, the MMU 490 provides one or moretranslation lookaside buffers (TLBs) for performing translation ofvirtual addresses into physical addresses in the memory 304.

FIG. 4B illustrates a memory partition unit 380 of the PPU 300 of FIG.3, in accordance with an embodiment. As shown in FIG. 4B, the memorypartition unit 380 includes a Raster Operations (ROP) unit 450, a leveltwo (L2) cache 460, and a memory interface 470. The memory interface 470is coupled to the memory 304. Memory interface 470 may implement 32, 64,128, 1024-bit data buses, or the like, for high-speed data transfer. Inan embodiment, the PPU 300 incorporates U memory interfaces 470, onememory interface 470 per pair of memory partition units 380, where eachpair of memory partition units 380 is connected to a correspondingmemory device 304. For example, PPU 300 may be connected to up to Ymemory devices 304, such as high bandwidth memory stacks or graphicsdouble-data-rate, version 5, synchronous dynamic random access memory,or other types of persistent storage.

In an embodiment, the memory interface 470 implements an HBM2 memoryinterface and Y equals half U. In an embodiment, the HBM2 memory stacksare located on the same physical package as the PPU 300, providingsubstantial power and area savings compared with conventional GDDR5SDRAM systems. In an embodiment, each HBM2 stack includes four memorydies and Y equals 4, with HBM2 stack including two 128-bit channels perdie for a total of 8 channels and a data bus width of 1024 bits.

In an embodiment, the memory 304 supports Single-Error CorrectingDouble-Error Detecting (SECDED) Error Correction Code (ECC) to protectdata. ECC provides higher reliability for compute applications that aresensitive to data corruption. Reliability is especially important inlarge-scale cluster computing environments where PPUs 300 process verylarge datasets and/or run applications for extended periods.

In an embodiment, the PPU 300 implements a multi-level memory hierarchy.In an embodiment, the memory partition unit 380 supports a unifiedmemory to provide a single unified virtual address space for CPU and PPU300 memory, enabling data sharing between virtual memory systems. In anembodiment the frequency of accesses by a PPU 300 to memory located onother processors is traced to ensure that memory pages are moved to thephysical memory of the PPU 300 that is accessing the pages morefrequently. In an embodiment, the NVLink 310 supports addresstranslation services allowing the PPU 300 to directly access a CPU'spage tables and providing full access to CPU memory by the PPU 300.

In an embodiment, copy engines transfer data between multiple PPUs 300or between PPUs 300 and CPUs. The copy engines can generate page faultsfor addresses that are not mapped into the page tables. The memorypartition unit 380 can then service the page faults, mapping theaddresses into the page table, after which the copy engine can performthe transfer. In a conventional system, memory is pinned (e.g.,non-pageable) for multiple copy engine operations between multipleprocessors, substantially reducing the available memory. With hardwarepage faulting, addresses can be passed to the copy engines withoutworrying if the memory pages are resident, and the copy process istransparent.

Data from the memory 304 or other system memory may be fetched by thememory partition unit 380 and stored in the L2 cache 460, which islocated on-chip and is shared between the various GPCs 350. As shown,each memory partition unit 380 includes a portion of the L2 cache 460associated with a corresponding memory device 304. Lower level cachesmay then be implemented in various units within the GPCs 350. Forexample, each of the SMs 440 may implement a level one (L1) cache. TheL1 cache is private memory that is dedicated to a particular SM 440.Data from the L2 cache 460 may be fetched and stored in each of the L1caches for processing in the functional units of the SMs 440. The L2cache 460 is coupled to the memory interface 470 and the XBar 370.

The ROP unit 450 performs graphics raster operations related to pixelcolor, such as color compression, pixel blending, and the like. The ROPunit 450 also implements depth testing in conjunction with the rasterengine 425, receiving a depth for a sample location associated with apixel fragment from the culling engine of the raster engine 425. Thedepth is tested against a corresponding depth in a depth buffer for asample location associated with the fragment. If the fragment passes thedepth test for the sample location, then the ROP unit 450 updates thedepth buffer and transmits a result of the depth test to the rasterengine 425. It will be appreciated that the number of memory partitionunits 380 may be different than the number of GPCs 350 and, therefore,each ROP unit 450 may be coupled to each of the GPCs 350. The ROP unit450 tracks packets received from the different GPCs 350 and determineswhich GPC 350 that a result generated by the ROP unit 450 is routed tothrough the Xbar 370. Although the ROP unit 450 is included within thememory partition unit 380 in FIG. 4B, in other embodiment, the ROP unit450 may be outside of the memory partition unit 380. For example, theROP unit 450 may reside in the GPC 350 or another unit.

FIG. 5A illustrates the streaming multi-processor 440 of FIG. 4A, inaccordance with an embodiment. As shown in FIG. 5A, the SM 440 includesan instruction cache 505, one or more scheduler units 510, a registerfile 520, one or more processing cores 550, one or more special functionunits (SFUs) 552, one or more load/store units (LSUs) 554, aninterconnect network 580, a shared memory/L1 cache 570.

As described above, the work distribution unit 325 dispatches tasks forexecution on the GPCs 350 of the PPU 300. The tasks are allocated to aparticular DPC 420 within a GPC 350 and, if the task is associated witha shader program, the task may be allocated to an SM 440. The schedulerunit 510 receives the tasks from the work distribution unit 325 andmanages instruction scheduling for one or more thread blocks assigned tothe SM 440. The scheduler unit 510 schedules thread blocks for executionas warps of parallel threads, where each thread block is allocated atleast one warp. In an embodiment, each warp executes 32 threads. Thescheduler unit 510 may manage a plurality of different thread blocks,allocating the warps to the different thread blocks and then dispatchinginstructions from the plurality of different cooperative groups to thevarious functional units (e.g., cores 550, SFUs 552, and LSUs 554)during each clock cycle.

Cooperative Groups is a programming model for organizing groups ofcommunicating threads that allows developers to express the granularityat which threads are communicating, enabling the expression of richer,more efficient parallel decompositions. Cooperative launch APIs supportsynchronization amongst thread blocks for the execution of parallelalgorithms. Conventional programming models provide a single, simpleconstruct for synchronizing cooperating threads: a barrier across allthreads of a thread block (e.g., the syncthreads( ) function). However,programmers would often like to define groups of threads at smaller thanthread block granularities and synchronize within the defined groups toenable greater performance, design flexibility, and software reuse inthe form of collective group-wide function interfaces.

Cooperative Groups enables programmers to define groups of threadsexplicitly at sub-block (e.g., as small as a single thread) andmulti-block granularities, and to perform collective operations such assynchronization on the threads in a cooperative group. The programmingmodel supports clean composition across software boundaries, so thatlibraries and utility functions can synchronize safely within theirlocal context without having to make assumptions about convergence.Cooperative Groups primitives enable new patterns of cooperativeparallelism, including producer-consumer parallelism, opportunisticparallelism, and global synchronization across an entire grid of threadblocks.

A dispatch unit 515 is configured to transmit instructions to one ormore of the functional units. In the embodiment, the scheduler unit 510includes two dispatch units 515 that enable two different instructionsfrom the same warp to be dispatched during each clock cycle. Inalternative embodiments, each scheduler unit 510 may include a singledispatch unit 515 or additional dispatch units 515.

Each SM 440 includes a register file 520 that provides a set ofregisters for the functional units of the SM 440. In an embodiment, theregister file 520 is divided between each of the functional units suchthat each functional unit is allocated a dedicated portion of theregister file 520. In another embodiment, the register file 520 isdivided between the different warps being executed by the SM 440. Theregister file 520 provides temporary storage for operands connected tothe data paths of the functional units.

Each SM 440 comprises L processing cores 550. In an embodiment, the SM440 includes a large number (e.g., 128, etc.) of distinct processingcores 550. Each core 550 may include a fully-pipelined,single-precision, double-precision, and/or mixed precision processingunit that includes a floating point arithmetic logic unit and an integerarithmetic logic unit. In an embodiment, the floating point arithmeticlogic units implement the IEEE 754-2008 standard for floating pointarithmetic. In an embodiment, the cores 550 include 64 single-precision(32-bit) floating point cores, 64 integer cores, 32 double-precision(64-bit) floating point cores, and 8 tensor cores.

Tensor cores configured to perform matrix operations, and, in anembodiment, one or more tensor cores are included in the cores 550. Inparticular, the tensor cores are configured to perform deep learningmatrix arithmetic, such as convolution operations for neural networktraining and inferencing. In an embodiment, each tensor core operates ona 4×4 matrix and performs a matrix multiply and accumulate operationD=A×B+C, where A, B, C, and D are 4×4 matrices.

In an embodiment, the matrix multiply inputs A and B are 16-bit floatingpoint matrices, while the accumulation matrices C and D may be 16-bitfloating point or 32-bit floating point matrices. Tensor Cores operateon 16-bit floating point input data with 32-bit floating pointaccumulation. The 16-bit floating point multiply requires 64 operationsand results in a full precision product that is then accumulated using32-bit floating point addition with the other intermediate products fora 4×4×4 matrix multiply. In practice, Tensor Cores are used to performmuch larger two-dimensional or higher dimensional matrix operations,built up from these smaller elements. An API, such as CUDA 9 C++ API,exposes specialized matrix load, matrix multiply and accumulate, andmatrix store operations to efficiently use Tensor Cores from a CUDA-C++program. At the CUDA level, the warp-level interface assumes 16×16 sizematrices spanning all 32 threads of the warp.

Each SM 440 also comprises M SFUs 552 that perform special functions(e.g., attribute evaluation, reciprocal square root, and the like). Inan embodiment, the SFUs 552 may include a tree traversal unit configuredto traverse a hierarchical tree data structure. In an embodiment, theSFUs 552 may include texture unit configured to perform texture mapfiltering operations. In an embodiment, the texture units are configuredto load texture maps (e.g., a 2D array of texels) from the memory 304and sample the texture maps to produce sampled texture values for use inshader programs executed by the SM 440. In an embodiment, the texturemaps are stored in the shared memory/L1 cache 470. The texture unitsimplement texture operations such as filtering operations using mip-maps(e.g., texture maps of varying levels of detail). In an embodiment, eachSM 340 includes two texture units.

Each SM 440 also comprises NLSUs 554 that implement load and storeoperations between the shared memory/L1 cache 570 and the register file520. Each SM 440 includes an interconnect network 580 that connects eachof the functional units to the register file 520 and the LSU 554 to theregister file 520, shared memory/L1 cache 570. In an embodiment, theinterconnect network 580 is a crossbar that can be configured to connectany of the functional units to any of the registers in the register file520 and connect the LSUs 554 to the register file and memory locationsin shared memory/L1 cache 570.

The shared memory/L1 cache 570 is an array of on-chip memory that allowsfor data storage and communication between the SM 440 and the primitiveengine 435 and between threads in the SM 440. In an embodiment, theshared memory/L1 cache 570 comprises 128 KB of storage capacity and isin the path from the SM 440 to the memory partition unit 380. The sharedmemory/L1 cache 570 can be used to cache reads and writes. One or moreof the shared memory/L1 cache 570, L2 cache 460, and memory 304 arebacking stores.

Combining data cache and shared memory functionality into a singlememory block provides the best overall performance for both types ofmemory accesses. The capacity is usable as a cache by programs that donot use shared memory. For example, if shared memory is configured touse half of the capacity, texture and load/store operations can use theremaining capacity. Integration within the shared memory/L1 cache 570enables the shared memory/L1 cache 570 to function as a high-throughputconduit for streaming data while simultaneously providing high-bandwidthand low-latency access to frequently reused data.

When configured for general purpose parallel computation, a simplerconfiguration can be used compared with graphics processing.Specifically, the fixed function graphics processing units shown in FIG.3, are bypassed, creating a much simpler programming model.

In the general purpose parallel computation configuration, the workdistribution unit 325 assigns and distributes blocks of threads directlyto the DPCs 420. The threads in a block execute the same program, usinga unique thread ID in the calculation to ensure each thread generatesunique results, using the SM 440 to execute the program and performcalculations, shared memory/L1 cache 570 to communicate between threads,and the LSU 554 to read and write global memory through the sharedmemory/L1 cache 570 and the memory partition unit 380. When configuredfor general purpose parallel computation, the SM 440 can also writecommands that the scheduler unit 320 can use to launch new work on theDPCs 420.

The PPU 300 may be included in a desktop computer, a laptop computer, atablet computer, servers, supercomputers, a smart-phone (e.g., awireless, hand-held device), personal digital assistant (PDA), a digitalcamera, a vehicle, a head mounted display, a hand-held electronicdevice, and the like. In an embodiment, the PPU 300 is embodied on asingle semiconductor substrate. In another embodiment, the PPU 300 isincluded in a system-on-a-chip (SoC) along with one or more otherdevices such as additional PPUs 300, the memory 204, a reducedinstruction set computer (RISC) CPU, a memory management unit (MMU), adigital-to-analog converter (DAC), and the like.

In an embodiment, the PPU 300 may be included on a graphics card thatincludes one or more memory devices 304. The graphics card may beconfigured to interface with a PCIe slot on a motherboard of a desktopcomputer. In yet another embodiment, the PPU 300 may be an integratedgraphics processing unit (iGPU) or parallel processor included in thechipset of the motherboard.

Exemplary Computing System

Systems with multiple GPUs and CPUs are used in a variety of industriesas developers expose and leverage more parallelism in applications suchas artificial intelligence computing. High-performance GPU-acceleratedsystems with tens to many thousands of compute nodes are deployed indata centers, research facilities, and supercomputers to solve everlarger problems. As the number of processing devices within thehigh-performance systems increases, the communication and data transfermechanisms need to scale to support the increased bandwidth.

FIG. 5B is a conceptual diagram of a processing system 500 implementedusing the PPU 300 of FIG. 3, in accordance with an embodiment. Theexemplary system 565 may be configured to implement the methods 130 and230 shown in FIGS. 1B and 2B, respectively. The processing system 500includes a CPU 530, switch 510, and multiple PPUs 300 each andrespective memories 304. The NVLink 310 provides high-speedcommunication links between each of the PPUs 300. Although a particularnumber of NVLink 310 and interconnect 302 connections are illustrated inFIG. 5B, the number of connections to each PPU 300 and the CPU 530 mayvary. The switch 510 interfaces between the interconnect 302 and the CPU530. The PPUs 300, memories 304, and NVLinks 310 may be situated on asingle semiconductor platform to form a parallel processing module 525.In an embodiment, the switch 510 supports two or more protocols tointerface between various different connections and/or links.

In another embodiment (not shown), the NVLink 310 provides one or morehigh-speed communication links between each of the PPUs 300 and the CPU530 and the switch 510 interfaces between the interconnect 302 and eachof the PPUs 300. The PPUs 300, memories 304, and interconnect 302 may besituated on a single semiconductor platform to form a parallelprocessing module 525. In yet another embodiment (not shown), theinterconnect 302 provides one or more communication links between eachof the PPUs 300 and the CPU 530 and the switch 510 interfaces betweeneach of the PPUs 300 using the NVLink 310 to provide one or morehigh-speed communication links between the PPUs 300. In anotherembodiment (not shown), the NVLink 310 provides one or more high-speedcommunication links between the PPUs 300 and the CPU 530 through theswitch 510. In yet another embodiment (not shown), the interconnect 302provides one or more communication links between each of the PPUs 300directly. One or more of the NVLink 310 high-speed communication linksmay be implemented as a physical NVLink interconnect or either anon-chip or on-die interconnect using the same protocol as the NVLink310.

In the context of the present description, a single semiconductorplatform may refer to a sole unitary semiconductor-based integratedcircuit fabricated on a die or chip. It should be noted that the termsingle semiconductor platform may also refer to multi-chip modules withincreased connectivity which simulate on-chip operation and makesubstantial improvements over utilizing a conventional busimplementation. Of course, the various circuits or devices may also besituated separately or in various combinations of semiconductorplatforms per the desires of the user. Alternately, the parallelprocessing module 525 may be implemented as a circuit board substrateand each of the PPUs 300 and/or memories 304 may be packaged devices. Inan embodiment, the CPU 530, switch 510, and the parallel processingmodule 525 are situated on a single semiconductor platform.

In an embodiment, the signaling rate of each NVLink 310 is 20 to 25Gigabits/second and each PPU 300 includes six NVLink 310 interfaces (asshown in FIG. 5B, five NVLink 310 interfaces are included for each PPU300). Each NVLink 310 provides a data transfer rate of 25Gigabytes/second in each direction, with six links providing 300Gigabytes/second. The NVLinks 310 can be used exclusively for PPU-to-PPUcommunication as shown in FIG. 5B, or some combination of PPU-to-PPU andPPU-to-CPU, when the CPU 530 also includes one or more NVLink 310interfaces.

In an embodiment, the NVLink 310 allows direct load/store/atomic accessfrom the CPU 530 to each PPU's 300 memory 304. In an embodiment, theNVLink 310 supports coherency operations, allowing data read from thememories 304 to be stored in the cache hierarchy of the CPU 530,reducing cache access latency for the CPU 530. In an embodiment, theNVLink 310 includes support for Address Translation Services (ATS),allowing the PPU 300 to directly access page tables within the CPU 530.One or more of the NVLinks 310 may also be configured to operate in alow-power mode.

FIG. 5C illustrates an exemplary system 565 in which the variousarchitecture and/or functionality of the various previous embodimentsmay be implemented. The exemplary system 565 may be configured toimplement the methods 130 and 230 shown in FIGS. 1B and 2B,respectively.

As shown, a system 565 is provided including at least one centralprocessing unit 530 that is connected to a communication bus 575. Thecommunication bus 575 may be implemented using any suitable protocol,such as PCI (Peripheral Component Interconnect), PCI-Express, AGP(Accelerated Graphics Port), HyperTransport, or any other bus orpoint-to-point communication protocol(s). The system 565 also includes amain memory 540. Control logic (software) and data are stored in themain memory 540 which may take the form of random access memory (RAM).

The system 565 also includes input devices 560, the parallel processingsystem 525, and display devices 545, e.g. a conventional CRT (cathoderay tube), LCD (liquid crystal display), LED (light emitting diode),plasma display or the like. User input may be received from the inputdevices 560, e.g., keyboard, mouse, touchpad, microphone, and the like.Each of the foregoing modules and/or devices may even be situated on asingle semiconductor platform to form the system 565. Alternately, thevarious modules may also be situated separately or in variouscombinations of semiconductor platforms per the desires of the user.

Further, the system 565 may be coupled to a network (e.g., atelecommunications network, local area network (LAN), wireless network,wide area network (WAN) such as the Internet, peer-to-peer network,cable network, or the like) through a network interface 535 forcommunication purposes.

The system 565 may also include a secondary storage (not shown). Thesecondary storage 610 includes, for example, a hard disk drive and/or aremovable storage drive, representing a floppy disk drive, a magnetictape drive, a compact disk drive, digital versatile disk (DVD) drive,recording device, universal serial bus (USB) flash memory. The removablestorage drive reads from and/or writes to a removable storage unit in awell-known manner.

Computer programs, or computer control logic algorithms, may be storedin the main memory 540 and/or the secondary storage. Such computerprograms, when executed, enable the system 565 to perform variousfunctions. The memory 540, the storage, and/or any other storage arepossible examples of computer-readable media.

The architecture and/or functionality of the various previous figuresmay be implemented in the context of a general computer system, acircuit board system, a game console system dedicated for entertainmentpurposes, an application-specific system, and/or any other desiredsystem. For example, the system 565 may take the form of a desktopcomputer, a laptop computer, a tablet computer, servers, supercomputers,a smart-phone (e.g., a wireless, hand-held device), personal digitalassistant (PDA), a digital camera, a vehicle, a head mounted display, ahand-held electronic device, a mobile phone device, a television,workstation, game consoles, embedded system, and/or any other type oflogic.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

Graphics Processing Pipeline

In an embodiment, the PPU 300 comprises a graphics processing unit(GPU). The PPU 300 is configured to receive commands that specify shaderprograms for processing graphics data. Graphics data may be defined as aset of primitives such as points, lines, triangles, quads, trianglestrips, and the like. Typically, a primitive includes data thatspecifies a number of vertices for the primitive (e.g., in a model-spacecoordinate system) as well as attributes associated with each vertex ofthe primitive. The PPU 300 can be configured to process the graphicsprimitives to generate a frame buffer (e.g., pixel data for each of thepixels of the display).

An application writes model data for a scene (e.g., a collection ofvertices and attributes) to a memory such as a system memory or memory304. The model data defines each of the objects that may be visible on adisplay. The application then makes an API call to the driver kernelthat requests the model data to be rendered and displayed. The driverkernel reads the model data and writes commands to the one or morestreams to perform operations to process the model data. The commandsmay reference different shader programs to be implemented on the SMs 440of the PPU 300 including one or more of a vertex shader, hull shader,domain shader, geometry shader, and a pixel shader. For example, one ormore of the SMs 440 may be configured to execute a vertex shader programthat processes a number of vertices defined by the model data. In anembodiment, the different SMs 440 may be configured to execute differentshader programs concurrently. For example, a first subset of SMs 440 maybe configured to execute a vertex shader program while a second subsetof SMs 440 may be configured to execute a pixel shader program. Thefirst subset of SMs 440 processes vertex data to produce processedvertex data and writes the processed vertex data to the L2 cache 460and/or the memory 304. After the processed vertex data is rasterized(e.g., transformed from three-dimensional data into two-dimensional datain screen space) to produce fragment data, the second subset of SMs 440executes a pixel shader to produce processed fragment data, which isthen blended with other processed fragment data and written to the framebuffer in memory 304. The vertex shader program and pixel shader programmay execute concurrently, processing different data from the same scenein a pipelined fashion until all of the model data for the scene hasbeen rendered to the frame buffer. Then, the contents of the framebuffer are transmitted to a display controller for display on a displaydevice.

FIG. 6 is a conceptual diagram of a graphics processing pipeline 600implemented by the PPU 300 of FIG. 3, in accordance with an embodiment.The graphics processing pipeline 600 is an abstract flow diagram of theprocessing steps implemented to generate 2D computer-generated imagesfrom 3D geometry data. As is well-known, pipeline architectures mayperform long latency operations more efficiently by splitting up theoperation into a plurality of stages, where the output of each stage iscoupled to the input of the next successive stage. Thus, the graphicsprocessing pipeline 600 receives input data 601 that is transmitted fromone stage to the next stage of the graphics processing pipeline 600 togenerate output data 602. In an embodiment, the graphics processingpipeline 600 may represent a graphics processing pipeline defined by theOpenGL® API. As an option, the graphics processing pipeline 600 may beimplemented in the context of the functionality and architecture of theprevious Figures and/or any subsequent Figure(s).

As shown in FIG. 6, the graphics processing pipeline 600 comprises apipeline architecture that includes a number of stages. The stagesinclude, but are not limited to, a data assembly stage 610, a vertexshading stage 620, a primitive assembly stage 630, a geometry shadingstage 640, a viewport scale, cull, and clip (VSCC) stage 650, arasterization stage 660, a fragment shading stage 670, and a rasteroperations stage 680. In an embodiment, the input data 601 comprisescommands that configure the processing units to implement the stages ofthe graphics processing pipeline 600 and geometric primitives (e.g.,points, lines, triangles, quads, triangle strips or fans, etc.) to beprocessed by the stages. The output data 602 may comprise pixel data(e.g., color data) that is copied into a frame buffer or other type ofsurface data structure in a memory.

The data assembly stage 610 receives the input data 601 that specifiesvertex data for high-order surfaces, primitives, or the like. The dataassembly stage 610 collects the vertex data in a temporary storage orqueue, such as by receiving a command from the host processor thatincludes a pointer to a buffer in memory and reading the vertex datafrom the buffer. The vertex data is then transmitted to the vertexshading stage 620 for processing.

The vertex shading stage 620 processes vertex data by performing a setof operations (e.g., a vertex shader or a program) once for each of thevertices. Vertices may be, e.g., specified as a 4-coordinate vector(e.g., <x, y, z, w>) associated with one or more vertex attributes(e.g., color, texture coordinates, surface normal, etc.). The vertexshading stage 620 may manipulate individual vertex attributes such asposition, color, texture coordinates, and the like. In other words, thevertex shading stage 620 performs operations on the vertex coordinatesor other vertex attributes associated with a vertex. Such operationscommonly including lighting operations (e.g., modifying color attributesfor a vertex) and transformation operations (e.g., modifying thecoordinate space for a vertex). For example, vertices may be specifiedusing coordinates in an object-coordinate space, which are transformedby multiplying the coordinates by a matrix that translates thecoordinates from the object-coordinate space into a world space or anormalized-device-coordinate (NCD) space. The vertex shading stage 620generates transformed vertex data that is transmitted to the primitiveassembly stage 630.

The primitive assembly stage 630 collects vertices output by the vertexshading stage 620 and groups the vertices into geometric primitives forprocessing by the geometry shading stage 640. For example, the primitiveassembly stage 630 may be configured to group every three consecutivevertices as a geometric primitive (e.g., a triangle) for transmission tothe geometry shading stage 640. In some embodiments, specific verticesmay be reused for consecutive geometric primitives (e.g., twoconsecutive triangles in a triangle strip may share two vertices). Theprimitive assembly stage 630 transmits geometric primitives (e.g., acollection of associated vertices) to the geometry shading stage 640.

The geometry shading stage 640 processes geometric primitives byperforming a set of operations (e.g., a geometry shader or program) onthe geometric primitives. Tessellation operations may generate one ormore geometric primitives from each geometric primitive. In other words,the geometry shading stage 640 may subdivide each geometric primitiveinto a finer mesh of two or more geometric primitives for processing bythe rest of the graphics processing pipeline 600. The geometry shadingstage 640 transmits geometric primitives to the viewport SCC stage 650.

In an embodiment, the graphics processing pipeline 600 may operatewithin a streaming multiprocessor and the vertex shading stage 620, theprimitive assembly stage 630, the geometry shading stage 640, thefragment shading stage 670, and/or hardware/software associatedtherewith, may sequentially perform processing operations. Once thesequential processing operations are complete, in an embodiment, theviewport SCC stage 650 may utilize the data. In an embodiment, primitivedata processed by one or more of the stages in the graphics processingpipeline 600 may be written to a cache (e.g. L1 cache, a vertex cache,etc.). In this case, in an embodiment, the viewport SCC stage 650 mayaccess the data in the cache. In an embodiment, the viewport SCC stage650 and the rasterization stage 660 are implemented as fixed functioncircuitry.

The viewport SCC stage 650 performs viewport scaling, culling, andclipping of the geometric primitives. Each surface being rendered to isassociated with an abstract camera position. The camera positionrepresents a location of a viewer looking at the scene and defines aviewing frustum that encloses the objects of the scene. The viewingfrustum may include a viewing plane, a rear plane, and four clippingplanes. Any geometric primitive entirely outside of the viewing frustummay be culled (e.g., discarded) because the geometric primitive will notcontribute to the final rendered scene. Any geometric primitive that ispartially inside the viewing frustum and partially outside the viewingfrustum may be clipped (e.g., transformed into a new geometric primitivethat is enclosed within the viewing frustum. Furthermore, geometricprimitives may each be scaled based on a depth of the viewing frustum.All potentially visible geometric primitives are then transmitted to therasterization stage 660.

The rasterization stage 660 converts the 3D geometric primitives into 2Dfragments (e.g. capable of being utilized for display, etc.). Therasterization stage 660 may be configured to utilize the vertices of thegeometric primitives to setup a set of plane equations from whichvarious attributes can be interpolated. The rasterization stage 660 mayalso compute a coverage mask for a plurality of pixels that indicateswhether one or more sample locations for the pixel intercept thegeometric primitive. In an embodiment, z-testing may also be performedto determine if the geometric primitive is occluded by other geometricprimitives that have already been rasterized. The rasterization stage660 generates fragment data (e.g., interpolated vertex attributesassociated with a particular sample location for each covered pixel)that are transmitted to the fragment shading stage 670.

The fragment shading stage 670 processes fragment data by performing aset of operations (e.g., a fragment shader or a program) on each of thefragments. The fragment shading stage 670 may generate pixel data (e.g.,color values) for the fragment such as by performing lighting operationsor sampling texture maps using interpolated texture coordinates for thefragment. The fragment shading stage 670 generates pixel data that istransmitted to the raster operations stage 680.

The raster operations stage 680 may perform various operations on thepixel data such as performing alpha tests, stencil tests, and blendingthe pixel data with other pixel data corresponding to other fragmentsassociated with the pixel. When the raster operations stage 680 hasfinished processing the pixel data (e.g., the output data 602), thepixel data may be written to a render target such as a frame buffer, acolor buffer, or the like.

It will be appreciated that one or more additional stages may beincluded in the graphics processing pipeline 600 in addition to or inlieu of one or more of the stages described above. Variousimplementations of the abstract graphics processing pipeline mayimplement different stages. Furthermore, one or more of the stagesdescribed above may be excluded from the graphics processing pipeline insome embodiments (such as the geometry shading stage 640). Other typesof graphics processing pipelines are contemplated as being within thescope of the present disclosure. Furthermore, any of the stages of thegraphics processing pipeline 600 may be implemented by one or morededicated hardware units within a graphics processor such as PPU 300.Other stages of the graphics processing pipeline 600 may be implementedby programmable hardware units such as the SM 440 of the PPU 300.

The graphics processing pipeline 600 may be implemented via anapplication executed by a host processor, such as a CPU. In anembodiment, a device driver may implement an application programminginterface (API) that defines various functions that can be utilized byan application in order to generate graphical data for display. Thedevice driver is a software program that includes a plurality ofinstructions that control the operation of the PPU 300. The API providesan abstraction for a programmer that lets a programmer utilizespecialized graphics hardware, such as the PPU 300, to generate thegraphical data without requiring the programmer to utilize the specificinstruction set for the PPU 300. The application may include an API callthat is routed to the device driver for the PPU 300. The device driverinterprets the API call and performs various operations to respond tothe API call. In some instances, the device driver may performoperations by executing instructions on the CPU. In other instances, thedevice driver may perform operations, at least in part, by launchingoperations on the PPU 300 utilizing an input/output interface betweenthe CPU and the PPU 300. In an embodiment, the device driver isconfigured to implement the graphics processing pipeline 600 utilizingthe hardware of the PPU 300.

Various programs may be executed within the PPU 300 in order toimplement the various stages of the graphics processing pipeline 600.For example, the device driver may launch a kernel on the PPU 300 toperform the vertex shading stage 620 on one SM 440 (or multiple SMs440). The device driver (or the initial kernel executed by the PPU 400)may also launch other kernels on the PPU 400 to perform other stages ofthe graphics processing pipeline 600, such as the geometry shading stage640 and the fragment shading stage 670. In addition, some of the stagesof the graphics processing pipeline 600 may be implemented on fixed unithardware such as a rasterizer or a data assembler implemented within thePPU 400. It will be appreciated that results from one kernel may beprocessed by one or more intervening fixed function hardware unitsbefore being processed by a subsequent kernel on an SM 440.

Machine Learning

Deep neural networks (DNNs) developed on processors, such as the PPU 300have been used for diverse use cases, from self-driving cars to fasterdrug development, from automatic image captioning in online imagedatabases to smart real-time language translation in video chatapplications. Deep learning is a technique that models the neurallearning process of the human brain, continually learning, continuallygetting smarter, and delivering more accurate results more quickly overtime. A child is initially taught by an adult to correctly identify andclassify various shapes, eventually being able to identify shapeswithout any coaching. Similarly, a deep learning or neural learningsystem needs to be trained in object recognition and classification forit get smarter and more efficient at identifying basic objects, occludedobjects, etc., while also assigning context to objects.

At the simplest level, neurons in the human brain look at various inputsthat are received, importance levels are assigned to each of theseinputs, and output is passed on to other neurons to act upon. Anartificial neuron or perceptron is the most basic model of a neuralnetwork. In one example, a perceptron may receive one or more inputsthat represent various features of an object that the perceptron isbeing trained to recognize and classify, and each of these features isassigned a certain weight based on the importance of that feature indefining the shape of an object.

A deep neural network (DNN) model includes multiple layers of manyconnected nodes (e.g., perceptrons, Boltzmann machines, radial basisfunctions, convolutional layers, etc.) that can be trained with enormousamounts of input data to quickly solve complex problems with highaccuracy. In one example, a first layer of the DNN model breaks down aninput image of an automobile into various sections and looks for basicpatterns such as lines and angles. The second layer assembles the linesto look for higher level patterns such as wheels, windshields, andmirrors. The next layer identifies the type of vehicle, and the finalfew layers generate a label for the input image, identifying the modelof a specific automobile brand.

Once the DNN is trained, the DNN can be deployed and used to identifyand classify objects or patterns in a process known as inference.Examples of inference (the process through which a DNN extracts usefulinformation from a given input) include identifying handwritten numberson checks deposited into ATM machines, identifying images of friends inphotos, delivering movie recommendations to over fifty million users,identifying and classifying different types of automobiles, pedestrians,and road hazards in driverless cars, or translating human speech inreal-time.

During training, data flows through the DNN in a forward propagationphase until a prediction is produced that indicates a labelcorresponding to the input. If the neural network does not correctlylabel the input, then errors between the correct label and the predictedlabel are analyzed, and the weights are adjusted for each feature duringa backward propagation phase until the DNN correctly labels the inputand other inputs in a training dataset. Training complex neural networksrequires massive amounts of parallel computing performance, includingfloating-point multiplications and additions that are supported by thePPU 300. Inferencing is less compute-intensive than training, being alatency-sensitive process where a trained neural network is applied tonew inputs it has not seen before to classify images, translate speech,and generally infer new information.

Neural networks rely heavily on matrix math operations, and complexmulti-layered networks require tremendous amounts of floating-pointperformance and bandwidth for both efficiency and speed. With thousandsof processing cores, optimized for matrix math operations, anddelivering tens to hundreds of TFLOPS of performance, the PPU 300 is acomputing platform capable of delivering performance required for deepneural network-based artificial intelligence and machine learningapplications.

What is claimed is:
 1. A computer-implemented method, comprising:generating a task-specific training dataset by: rendering athree-dimensional (3D) object according to one or more differentparameters to produce one or more renderings of the 3D object; computingtask-specific training data corresponding to the one or more renderingsof the 3D object; and pairing at least one input image of a plurality ofinput images with the task-specific data to produce one or more testpairs, the plurality of input images depicting at least one rendering ofthe one or more renderings of the 3D object with a background image;training a neural network to detect the 3D object using thetask-specific training dataset, wherein the neural network receives atleast a portion of the plurality of input images while subsequent imagesin the plurality of input images are produced.
 2. Thecomputer-implemented method of claim 1, further comprising receiving atleast one of: data corresponding to a position of a light for renderingthe 3D object; data corresponding to an orientation of a light forrendering the 3D object; data corresponding to a color of the light forrendering the 3D object; or data corresponding to an intensity of thelight for rendering the 3D object.
 3. The computer-implemented method ofclaim 1, further comprising rendering one or more 3D geometric shapes toproduce one or more rendered two-dimensional (2D) geometric shapes,wherein the one or more rendered 2D geometric shapes are included in atleast one input image in the plurality of input images.
 4. Thecomputer-implemented method of claim 3, wherein the one or more rendered2D geometric shapes are omitted from the task-specific training data. 5.The computer-implemented method of claim 1, further comprising:rendering an additional 3D object to produce an additional rendered 3Dobject, wherein the rendered additional 3D object is included in atleast one input image in the plurality of input images; and computingadditional task-specific training data corresponding to the additionalrendered 3D object, wherein the additional task-specific training datais included in the test pairs that include the at least one input image.6. The computer-implemented method of claim 5, wherein the additionalrendered 3D object occludes a portion of the at least one rendering ofthe one or more renderings of the 3D object, and responsive to theportion being greater than a predetermined threshold value, modifyingthe task-specific training data corresponding to the at least onerendering of the one or more renderings of the 3D object.
 7. Thecomputer-implemented method of claim 1, wherein rendering the 3D objectcomprises rendering the 3D object within a 3D scene corresponding to thebackground image.
 8. The computer-implemented method of claim 1, furthercomprising: rendering a 3D scene to produce the background image; andcombining each one of the at least one rendering of the one or morerenderings of the 3D object and the background image to produce theplurality of input images.
 9. The computer-implemented method of claim1, wherein the task-specific training data defines a location anddimensions of a bounding shape enclosing the at least one rendering ofthe one or more renderings of the 3D object.
 10. Thecomputer-implemented method of claim 1, wherein the task-specifictraining data comprises an object identifier for each pixel that iscovered by the at least one rendering of the one or more renderings ofthe 3D object.
 11. The computer-implemented method of claim 1, whereinthe different parameters comprise at least one of a position or anorientation in a 3D space for rendering the one or more renderings ofthe 3D object.
 12. The computer-implemented method of claim 1, whereinrendering the one or more renderings of the 3D object comprises applyinga texture map to the 3D object.
 13. A system, comprising: a processorconfigured to generate a task-specific training dataset: rendering athree-dimensional (3D) object according to one or more differentparameters to produce one or more renderings of the 3D object; computingtask-specific training data corresponding to at least one of the one ormore renderings of the 3D object; and pairing at least one input imageof a plurality of input images with the task-specific data to produceone or more test pairs, the plurality of input images depicting at leastone rendering of the one or more renderings of the 3D object with abackground image; and a neural network that is trained to detect the 3Dobject using the task-specific training dataset, wherein the neuralnetwork receives at least a portion of the plurality of input imageswhile subsequent images in the plurality of input images are produced.14. The system of claim 13, wherein the processor is further configuredto render one or more 3D geometric shapes to produce one or morerendered two-dimensional (2D) geometric shapes, wherein the one or morerendered 2D geometric shapes are included in at least one input image inthe plurality of input images.
 15. The system of claim 14, wherein theone or more rendered 2D geometric shapes are omitted from thetask-specific training data.
 16. The system of claim 13, wherein theprocessor is further configured to: render an additional 3D object toproduce an additional rendered 3D object, wherein the renderedadditional 3D object is included in at least one input image in theplurality of input images; and compute additional task-specific trainingdata corresponding to the additional rendered 3D object, wherein theadditional task-specific training data is included in the test pairsthat include the at least one input image.
 17. The system of claim 16,wherein the additional rendered 3D object occludes a portion of the atleast one rendering of the one or more renderings of the 3D object, andresponsive to the portion being greater than a predetermined thresholdvalue, the task-specific training data corresponding to the at least onerendering of the one or more renderings of the 3D object is modified.18. A non-transitory, computer-readable storage medium storinginstructions that, when executed by a processor, cause the processor to:generate a task-specific training dataset by: rendering athree-dimensional (3D) object according to one or more differentparameters to produce one or more renderings of the 3D object; computingtask-specific training data corresponding to at least one of the one oneor more renderings of the 3D object; and pairing at least one inputimage of a plurality of input images with the task-specific data toproduce one or more test pairs, the plurality of input images depictingat least one rendering of the one or more renderings of the 3D objectwith a background image; and train a neural network to detect the 3Dobject using the task-specific training dataset, wherein the neuralnetwork receives at least a portion of the plurality of input imageswhile subsequent images in the plurality of input images are produced.19. The non-transitory, computer-readable storage medium of claim 18,further comprising rendering one or more 3D geometric shapes to produceone or more rendered two-dimensional (2D) geometric shapes, wherein theone or more rendered 2D geometric shapes are included in at least oneinput image in the plurality of input images.
 20. The non-transitory,computer-readable storage medium of claim 19, wherein the one or morerendered 2D geometric shapes are omitted from the task-specific trainingdata.